MNE-Python#
MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. Use it to perform offline analysis of recordings from your Wearable Sensing DSI-24, DSI-VR300, DSI-7, or DSI-Flex headsets.
Installation Required
Before starting, install MNE-Python following the official installation guide. These tutorials use MNE version 1.9.0 and demonstrate workflows with Wearable Sensing EDF files exported from DSI-Streamer.
Getting Started#
Learn how to work with Wearable Sensing EEG data in MNE-Python through step-by-step tutorials covering data loading, preprocessing, and analysis.
Tutorial Sections#
The tutorials are organized into two main sections: Core Operations and Data Processing.
Core Operations#
Start Here
Begin with loading your data, then configure channels and references for analysis.
Load Wearable Sensing Data - Load EDF files from DSI-Streamer
Channel Configuration - Set channel types, manage selections, and configure referencing
Data Processing#
Preprocessing Pipeline
Follow these tutorials in sequence for a complete preprocessing workflow:
Filter - Remove noise and isolate frequency bands
Artifacts - Clean data using ICA and rejection methods
Epochs - Extract event-related segments for analysis
Filtering - Apply frequency filters and resampling
Artifact Handling - Remove artifacts with ICA and rejection
Epoching & Event Handling - Create epochs and compute ERPs